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Record W2053092242 · doi:10.1121/1.4795786

Language dependent vowel representation in speech production

2013· article· en· W2053092242 on OpenAlexaff
Takashi Mitsuya, Fabienne Samson, Lucie Ménard, Kevin G. Munhall

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2013
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversité du Québec à MontréalQueen's University
FundersNational Institute on Deafness and Other Communication Disorders
KeywordsFormantVowelMid vowelPerceptionRelative articulationMathematicsRepresentation (politics)Speech recognitionAcousticsComputer sciencePsychologyPhysics

Abstract

fetched live from OpenAlex

The representation of speech goals was explored using an auditory feedback paradigm. When talkers produce vowels the formant structure of which is perturbed in real time, they compensate to preserve the intended goal. When vowel formants are shifted up or down in frequency, participants change the formant frequencies in the opposite direction to the feedback perturbation. In this experiment, the specificity of vowel representation was explored by examining the magnitude of vowel compensation when the second formant frequency of a vowel was perturbed for speakers of two different languages (English and French). Even though the target vowel was the same for both language groups, the pattern of compensation differed. French speakers compensated to smaller perturbations and made larger compensations overall. Moreover, French speakers modified the third formant in their vowels to strengthen the compensation even though the third formant was not perturbed. English speakers did not alter their third formant. Changes in the perceptual goodness ratings by the two groups of participants were consistent with the threshold to initiate vowel compensation in production. These results suggest that vowel goals not only specify the quality of the vowel but also the relationship of the vowel to the vowel space of the spoken language.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.889
Threshold uncertainty score0.599

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.342
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations30
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicPhonetics and Phonology ResearchFrench-language works237,207